The retrospective data analysis on the spectrum of nervous system diseases in children

doi:10.21203/rs.3.rs-1730790/v1

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The retrospective data analysis on the spectrum of nervous system diseases in children

https://doi.org/10.21203/rs.3.rs-1730790/v1

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Background

Nowadays, the development of diagnosis and treatment technology is constantly changing the pedigree and classification of nervous system diseases. The analysis of the changes of previous disease pedigrees is helpful to understand the changes brought by accurate diagnosis of diseases from the macro direction, and predict the changes of subsequent disease pedigrees and the direction of diagnosis and treatment.

Methods

The in-patients in neurology department from January 2012 to December 2020 in Hunan Children's Hospital were analyzed retrospectively. There were 36777 patients were enrolled in this study. The subsequent analysis was based on variables such as Age, Gender, Length of Stay (LoS), Monthly and Annual Number of Patients (MNoP and ANoP, respectively), and Average Daily Hospitalization Expense (ADHE). To assess the characteristics of neurology diseases, we applied a series of statistical tools such as numerical characteristics, boxplot, density chart, one way ANOVA, Kruskal-Wallis test, time-series plots and seasonally adjusted indices.

Results

The following conclusions were drawn from the statistical analysis of neurological disease. Firstly, neurological disease in children mainly appear between the ages of 4 and 8 years. Among various neurological diseases, benign intracranial hypertension had the lowest average onset age, and most patients with bacterial intracranial infection were infants. Some diseases have similar mean onset age, such as convulsion and febrile convulsion. Secondly, most patients with central nervous system autoimmune disease were women. Hereditary metabolic encephalopathy is highly similar in terms of treatment options, while viral intracranial infection and epilepsy is in contrast, with obvious heterogeneity in treatment. It was noted that some neurological diseases showed seasonality, for example, central nervous system infectious disease was more frequent in high temperature season, while autoimmune disease mainly appeared in autumn and winter. In addition, the number of patients hospitalized for intracranial infection and encephalopathy has decreased significantly in recent years, while that for central nervous system autoimmune disease and hereditary metabolic encephalopathy has been increasing year by year. Finally, we found obvious polycentric distributions in the density distributions of some diseases.

Conclusion

The study provided an epidemiological basis for common nervous system diseases, such as age of onset, number of cases and so on. The spectrum of nervous system diseases took place great changes. The number of patients with infection related diseases and uncertain diagnosis is decreasing, and the proportion of neuroimmune diseases and genetic metabolic diseases is increasing; The existence of disease multimodal model suggests that there is still a lack of understanding of the diagnosis and treatment of many diseases, which needs to be further improved, since accurate diagnosis helps to formulate individualized treatment plans and reasonably allocate medical resources; Moreover, it still lacks effective treatment for most genetic diseases; The seasonal characteristics of nervous system diseases suggest the need for the improvement of sanitation, living conditions and the awareness of daily health care.

nervous system diseases in children

descriptive analysis

heterogeneity analysis

seasonal and trend analysis

The children’s difficult and rare diseases in nervous system are very common, and many of them have unknown etiology, and lack of effective treatment strategies. Unclear diagnosis and unclear etiology bring a certain degree of inconvenience to patients, which leads to an increase in the mortality of patients and the proportion of sequelae [1]. In the past decade, with the gradual popularization of the concept of precision medicine and the improvement of diagnosis and treatment technology, many difficult and rare neurological diseases have been gradually recognized, diagnosed, and cured [2]. Also, the improvement of diagnosis and treatment technology largely depends on the continuous summary and exploration of previous cases, and on this basis, seek new breakthroughs.

In the present clinical practice, the spectrum of neurological diseases in children is still not perfect, which is greatly affected by the progress of modern medical diagnosis and treatment means. Especially in recent decades, with the gradual popularization of the concept of precision medical diagnosis and treatment, the spectrum of neurological diseases in children has changed greatly, and the treatment methods, clinical diagnosis, treatment costs and prognosis, etc. have also changed accordingly [3]. For example, viral encephalitis is a disease with a high diagnosis rate in pediatric neurology because of its diverse clinical manifestations, varying degrees of severity, and high heterogeneity. Its treatment includes antiviral therapy, symptomatic therapy, immunotherapy, and even plasma exchange, blood purification, etc. are required in critically ill patients. The treatment methods are different, and the treatment time, cost and prognosis are also very different. Among the patients previously diagnosed with viral encephalitis, a considerable number are auto-immune encephalitis or metabolic encephalopathy, which are often misdiagnosed and lead to excessive medical treatment, increasing the economic burden on patients and the society [4, 5].

Investigating the relevant literature, there is still a lack of systematic data analysis and summary on the spectrum of children's neurological diseases. Therefore, in this study, a systematic and comprehensive multi-dimensional retrospective analysis was conducted based on the data of diagnostic disease spectrum changes, correlation analysis of disease spectrum factors and clinical treatment costs of inpatients in the Department of Neurology of Hunan Children's Hospital from 2012 to 2020. Through this study, it is helpful to subdivide and improve the spectrum of neurological diseases in children, understand the development course of diseases, and assist clinicians to diagnose and treat patients' conditions accurately to some extent, so as to control and treat patients' conditions immediately and reduce the economic burden on patients and society.

2.1 Study design

This is a retrospective analysis of the clinical data of inpatients in the Department of Neurology of Hunan Children's Hospital from January 2012 to December 2020. This study was approved by the Medical Ethics Committee of Hunan Children's Hospital.

A total of 37069 desensitized original cases are collected. Note that in the original data set, there are: 1) 15 cases with missing gender; 2) 4 cases with unreasonable age, including 1 case with zero year and 3 cases older than 18 years; 3) 1 case with missing hospitalization expense; 4) 272 cases of non-nervous system diseases. Excluding these cases, we finally obtain 36777 valid cases having full information of interest.

In clinical medicine, it is known that the disease diagnosis usually depends on a series of outcomes such as the patient's clinical symptoms, auxiliary examination, and response to treatment. The accuracy of judgment is closely related to the latest medical conditions, the accessibility of diagnostic methods and doctors’ understanding level. To provide an updated relationship of disease diagnosis, we match, based on the literature [6] and clinical experience, the 36777 valid cases into a pedigree chart as shown in Fig. 1, which shows 20 disease clusters and their corresponding subtype diseases. The focus of this study is mainly on the encephalitis and encephalopathy with kinds of etiologies, including five clusters as follows:

(C1) Intracranial infection, including the viral, bacterial, fungal, and tuberculous intracranial infection, mycoplasma encephalitis, intracranial infection with unknown etiology and other encephalitis (except immune encephalitis);

(C2) Central nervous system autoimmune disease, including the myelin oligodendrocyte glycoprotein-antibody-associated disorder (MOG antibody-associated disorder), Autoimmune Glial Fibrillary Acidic Protein Astrocytopathy (GFAP astrocytopathy), anti-NMDAR encephalitis, overlapping antibody immune encephalitis, other antibody-mediated autoimmune encephalitis, central demyelinating disease, unclassified immune disease;

(C3) Hereditary metabolic encephalopathy, including mitochondrial encephalopathy and other inherited metabolic disease related encephalopathy;

(C4) Encephalopathy, which refers to a disease that is diagnosed with symptoms but not specifically classified;

(C5) Cerebrovascular disease, including stroke events such as cerebral infarction and intracranial hemorrhage;

Note that the encephalopathy and cerebrovascular diseases are no longer subdivided.

2.2 Variables description

We study the following variables, i.e., Age, Gender, Length of Stay (LoS), Monthly and Annual Number of Patients (MNoP and ANoP, respectively), and Average Daily Hospitalization Expense (ADHE). Note that age is measured by year, whose value is reserved to two decimal places; Gender takes 1 for male and 0 for female; LoS is based on days, and the day of admission is not included LoS; the number of hospitalized patients is based on the time of admission; ADHE is based on CNY, which retains integer part.

2.3 A summary on the related statistical analysis

Based on these collected data, we conduct a statistical analysis on the pediatric neurology diseases. It consists of three parts:

In part 1, we conduct a descriptive analysis for the Age, Gender, LoS and ADHE of all subtype diseases. Their sample means are presented in Table 1. Note that for Gender, we report its relative frequencies (%) as it is a categorical;

In part 2, we check the relationship and differences between these subtype diseases by the boxplot and t-test analysis. The probability density distribution charts are also plotted. In further analysis of the difference in clusters, one way ANOVA is used. Considering the possible non-normality of data, we compare this method with the Kruskal-Wallis test to ensure the reliability of the results. The p-value is two-tailed. The difference is significant if the p-value is less than 0.05;

In part 3, we investigate the seasonal characteristics and long-term trends, aiming to provide a more comprehensive analysis on the seasonality and the progression of encephalitis and encephalopathy with kinds of etiologies. Here, we use the statistical tools including time-series plots and seasonally adjusted indices.

In this section, we report the related results. It is worth mentioning that we here do not list the results related to subtype diseases with sample sizes less than 30 for the sake of making reliable statistical analysis.

3.1 Results of descriptive analysis

Table 1 summarizes the basic characteristics of neurological diseases in children.

The results are as follow: For Age, the period with high occurrence of neurological diseases in children is mainly concentrated in the range of 4–8 years. Note that benign intracranial hypertension (297 cases) has the lowest mean onset age at 0.83 years. And the most popular neurological disease in children is paroxysmal disease (24613 cases). Epilepsy (15190 cases) is the most common subtype disease, with a mean onset age of 3.82 years. It is followed by febrile convulsion (7336 cases) and then convulsion (gastroenteritis / diarrhea) (1314 cases), with the mean onset ages of 2.35 years and 1.55 years, respectively. Obviously, the age of convulsion (gastroenteritis / diarrhea) is lower than that of the first two. Lastly, the average age of 773 cases convulsion (others) is 2.47 years. In another common disease - intracranial infection, the mean onset age of bacterial intracranial infection (1557 cases) is also significantly low, at 1.57 years. The onset age of intracranial infection of unknown etiology (863 cases) is about 5.3 years, similar to 5.71 years for that of viral intracranial infection (3652 cases). For central nervous system autoimmune disease (537 cases), the high incidence age is concentrated in the range of 6–8 years. Moreover, there are 639 cases of myasthenia gravis with an average onset age of 4.28 years and GBS, MFS syndrome (131 cases) with that of 5.2 years. For myositis (168 cases) and facial paralysis (909 cases), the average onset age is 6.15 and 4.3 years, respectively. Also, mitochondrial encephalopathy has 42 cases with mean age of 6.47 years.

For Gender, the proportion of male patients is higher than that of female patients in most diseases, except for MOG antibody-associated disorder, anti-NMDAR encephalitis, unclassified immune disease, neuromuscular junction disease and convulsions (gastroenteritis/diarrhea).

For LoS, it varies greatly among different diseases. The LoS for central nervous system autoimmune disease is the highest among all of these diseases, locating in 16–22 days. For diseases such as febrile convulsions, childhood gastroenteritis with benign convulsions, and unexplained convulsions, the LoS of those diseases are shorter, since patients are discharged after the symptom’s improvement.

For ADHE, pediatric neurology diseases are generally high, about 1000 CNY. Combining the two factors of LoS and ADHE, the LoS of those diseases and their corresponding ADHE associated with such treatments are shorter, within 10 days and less than 1000 CNY respectively. Moreover, the shorter LoS for convulsion is mainly influenced by the fact that patients are discharged after the symptom’s improvement and it only can be achieved by symptomatic treatments. The corresponding ADHE associated with such treatments are also relatively low, which is less than 1000 CNY per day. On the contrary, the LoS of intracranial infection (except for intracranial infection of unknown etiology), GBS, MFS syndrome and central nervous system autoimmune disease is longer than 10 days, and the ADHE of central nervous system autoimmune disease is above 1300 CNY.

Table 1

Baseline characteristics of 36777 children with neurological diseases according to data set
Neurological diseases	Age	Gender (Male)	LoS	ADHE
Intracranial infection
Viral intracranial infection	5.71	66%	13.37	948.30
Bacterial intracranial infection	1.57	64%	21.27	995.36
Intracranial infection of unknown etiology	5.30	65%	8.40	980.26
Central nervous system autoimmune disease
MOG antibody-associated disorder	6.69	41%	16.13	1462.04
anti-NMDAR encephalitis	7.74	47%	22.84	1333.07
Central demyelinating disease	6.15	57%	17.71	1366.31
Unclassified immune disease	7.45	50%	17.43	1570.88
Hereditary metabolic encephalopathy
Mitochondrial encephalopathy	6.47	62%	9.90	996.47
Other inherited metabolic disease related encephalopathy	3.30	55%	9.80	991.17
Other encephalopathy
Encephalopathy	1.65	63%	14.37	1083.59
Cerebrovascular disease
Cerebrovascular disease	5.40	56%	10.69	1029.34
White matter lesion
White matter lesion	5.69	69%	7.81	999.53
Cranial/Brain malformation (structural)
Cranial/Brain malformation (structural)	2.90	57%	7.65	858.48
Developmental disorder (neuropsychiatric)
Developmental disorder(neuropsychiatric)	6.60	64%	3.56	1268.70
Benign intracranial hypertension
Benign intracranial hypertension	0.83	64%	6.01	940.78
Traumatic brain injury
Traumatic brain injury	2.50	73%	12.69	1089.49
Tumour
Tumour	5.51	61%	5.26	1472.81
Poisoning
Poisoning	4.46	55%	10.54	1262.33
Paroxysmal disease
Epilepsy	3.82	56%	8.09	732.29
Febrile convulsion	2.35	65%	5.63	944.35
Convulsion (gastroenteritis / diarrhea)	1.55	45%	5.01	925.64
Convulsion (others)	2.47	59%	5.82	972.36
Migraine / Vascular neuromodulation disorder
Migraine / Vascular neuromodulation disorder	8.40	63%	5.38	960.48
Dyskinesia / Dystonia
Dyskinesia / Dystonia	5.00	59%	8.16	1035.11
Peripheral nerve disease
GBS, MFS syndrome	5.20	58%	17.10	1408.00
Peripheral neuropathy (unclear diagnosis)	5.27	60%	12.51	1099.75
Facial paralysis
Facial paralysis	4.30	54%	10.95	716.23
Neuromuscular junction disease
Myasthenia Gravis	4.28	41%	8.07	744.01
Muscle Disease
Myositis	6.15	79%	6.62	886.67

3.2 Results of heterogeneity analysis

To examine the age differences between subtype diseases, we report the boxplot results in Fig. 2, which shows the shape of Age for the intracranial infections, central nervous system autoimmune disease and hereditary metabolic encephalopathy. Here, we do not list the results of LoS and ADHE for the sake of saving space.

It turns out that in intracranial infectious disease, most subtype diseases have a broader onset age span of disease and a weaker concentration, except for bacterial intracranial infection. Noted that there retains a particular boxplot for bacterial intracranial infection. The shape of its boxplot has no obvious rectangular box, with its upper and lower boundaries coincided, which indicates that the data are probably greatly biasedly distributed. And there are significant age differences between all pairs of subtype diseases.

For the central nervous system autoimmune disease, the age distributions of MOG antibody-associated disorders, anti-NMDAR encephalitis, and central demyelinating diseases are more concentrated between the lower quartile and the median. The median onset age of central demyelinating disease is relatively smallest. Notably, significant differences are only found in the pair of unclassified immune disease and central demyelinating disease. Meanwhile, these subtype diseases are relatively broad in age span, revealing that immune diseases can occur at all ages, especially school-aged children.

Lastly, it should be noted that the onset age of other inherited metabolic disease related encephalopathy is lower than that of mitochondrial encephalopathy in hereditary metabolic encephalopathy. When comparing the age difference between the two subtype diseases, it is also significant. Noted that these two subtype diseases also have extremely large age span, indicating that hereditary metabolic encephalopathy can be sick at any age.

Noting that the distribution of Age of the bacterial intracranial infection is greatly biased, we further plot the probability density distributions of its Age, LoS and ADHE in Fig. 3, which shows a non-unimodal phenomenon in some density functions of Age, LoS and ADHE. This implies that there may exist some additional subtype diseases in intracranial infection disease.

In Fig. 3, it is consistent with clinical data that the multimodal distribution and weaker concentration of LoS in bacterial intracranial infection. Generally speaking, the course of bacterial meningitis antibiotics is usually 2–4 weeks, some of the patients with subdural pus and ependymitis can last for 8 weeks or even longer. Furthermore, there are non-unimodal and tailing phenomena in the LoS and ADHE of other encephalitis (except immune encephalitis), showing that uncertain diagnosis may give rise to different treatment plans, courses of medical treatment and recovery time. Similarly, viral intracranial infection also has non-unimodal and tailing phenomenon in varying degrees.

In Fig. 4, it shows obvious polycentric distributions of Age and LoS for central demyelinating disease and unclassified immune disease. These wide range indicates that some new subtype diseases may be subdivided from them. Relatively speaking, there are more concentrated distributions of each characteristic for MOG antibody-associated disease and anti-NMDAR encephalitis. It is the result of precise treatment according to clinical features combined with the refinement of disease subtypes. However, these phenomena, still including certain degree of tailing and multi-centralization, are related to the severe clinical phenotypes and serious complications of some MOG antibody-associated disorder and anti-NMDAR encephalitis in clinical practice.

To detect the possible difference between more than two subtype diseases, we further carry out an analysis on these data by using both the one-way ANOVA and Kruskal-Wallis test. The related results are shown in Table 2. The results indicate by the one-way ANOVA that the distributions of Age, Gender, LoS, and ADHE in the subtype of intracranial infection and central nervous system autoimmune disease, except for Gender, differ significantly from each other. In contrast, hereditary metabolic encephalopathy differs significantly in the distribution of Age only. For the sake of stability, we also report the results of the Kruskal-Wallis test, which show similar observations as that of the one-way ANOVA.

Table 2

Analysis of variability in the basic characteristics of subtype within diseases
	Neurological diseases	Df	P-value (Age)	P-value (Gender)	P-value (LoS)	P-value (ADHE)
One way ANOVA	Intracranial infection	3	0.0000***	0.3470	0.0000***	0.0003***
	Central nervous system autoimmune disease	3	0.0018 **	0.1790	0.0375 *	0.0100 *
	Hereditary metabolic encephalopathy	1	0.0000***	0.376	0.941	0.13
Kruskal-Wallis test	Intracranial infection	3	0.0000***	0.3471	0.0000***	0.0000***
	Central nervous system autoimmune disease	3	0.0053 **	0.1789	0.0073 **	0.0131 *
	Hereditary metabolic encephalopathy	1	0.0000***	0.3752	0.3532	0.372

3.3 Results of seasonal and trend Analysis

To analyze the seasonal characteristics of these diseases, we plot the monthly number, which is the summation of numbers in the same month of years from 2012 to 2020, of patients of encephalitis and encephalopathy with kinds of etiologies.

Furthermore, we further compute the seasonal index of these diseases in Table 3. Combined with Fig. 5 and Table 3, they present that there are regular fluctuations in some diseases. Specifically, viral intracranial infection, bacterial intracranial infection and intracranial infection of unknown etiology have certain seasonal characteristics. In other words, these three subtype diseases have high incidence months, respectively, in June-July, July, and May-June. Most patients with intracranial infection develop symptoms in summer, which is more conducive to the transmission of infectious diseases with high temperature. For other encephalitis and encephalopathy with kinds of etiologies, there is no clear statistical evidence to suggest the existence of a dominant season. Other diseases have dominant seasons without clear statistical evidence. Moreover, subtype diseases of central nervous system autoimmune disease also have large fluctuations with monthly changes. Noting that January is the high incidence month of central demyelinating disease. For other diseases, there is no clear statistical basis to demonstrate a dominant season.

Table 3

Seasonal index of encephalopathy-related diseases
Dise	Jan.	Feb.	Mar.	Apr.	May.	Jun.	Jul.	Aug.	Sep.	Oct.	Nov.	Dec.
I
i	74.9%	48.0%	56.9%	94.6%	162.3%	216.5%	203.4%	93.0%	56.9%	66.7%	52.3%	74.6%
ii	93.3%	77.1%	86.3%	97.1%	113.3%	124.9%	159.5%	117.9%	84.0%	74.0%	83.2%	89.4%
iii	70.9%	50.1%	52.5%	101.4%	194.3%	194.3%	133.2%	91.6%	77.0%	73.3%	83.1%	78.2%
II
iv	\	75.00%	112.5%	150.0%	150.0%	37.5%	75.0%	150.0%	75.0%	225.0%	37.5%	112.5%
v	83.7%	139.5%%%%	83.7%	55.8%	111.6%	111.6%	55.8%	83.7%	195.4%	139.5%	55.8%	83.7%
vi	147.4%	122.9%	110.6%	110.5%	106.5%	86.0%	81.9%	53.2%	73.7%	118.8%	86.0%%	102.4%
vii	98.0%	57.1%	106.1%	98.0%	122.4%	122.4%	81.6%	40.8%	98.0%	138.8%	130.6%	106.1%
III
viii	28.6%	114.3%	57.1%	114.3%	85.7%	57.1%	114.3%	114.3%	200.0%	85.7%	85.7%	142.9%
ix	87.6%	108.2%	118.5%	82.4%	108.2%	82.4%	97.9%	87.6%	128.8%	123.6%	77.3%	97.9%
IV
x	135.0%	142.5%	120.0%	120.0%	67.5%	71.3%	90.0%	86.3%	112.5%	75.0%	63.8%	116.3%
V
xi	50.5%	63.2%	126.3%	189.5%	75.8%	164.2%	75.8%	101.1%	139.0%	50.5%	75.8%	88.4%
Note. I: Intracranial infection; II: Central nervous system autoimmune disease; III: Hereditary metabolic encephalopathy; VI: Other encephalopathy; V: Cerebrovascular disease; i: Viral intracranial infection; ii: Bacterial intracranial infection; iii: Intracranial infection of unknown etiology; iv: MOG antibody-associated disorder; v: anti-NMDAR encephalitis; vi: Central demyelinating disease; vii: Unclassified immune disease; viii: Mitochondrial encephalopathy; ix: Other inherited metabolic disease related encephalopathy; x: Encephalopathy; xi: Cerebrovascular disease

Next, to better analyze the trend of the diseases in the long term, we present the annual time series plots for each subtype disease from 2012 to 2020. Figure 6 shows a clear downward trend in the ANoP for intracranial infections. Among them, the decline in viral intracranial infection is similar to the overall trend.

In addition to central demyelinating disease, Fig. 7 shows an upward trend in the ANoP with central nervous system autoimmune disease. Furthermore, anti-NMDAR encephalitis and MOG antibody-associated disorder are new diagnostic diseases proposed in recent years. It is noted that there is still an obvious upward trend, though the ANoP relatively small, and it may continue to rise in the future. Therefore, it is of paramount importance that clarifying the specific etiology for the diagnosis and treatment to patients.

In Fig. 8, the ANoP with hereditary metabolic encephalopathy is increasing. Meanwhile, mitochondrial encephalopathy has increased significantly since 2020, which may be related to the improvement of clinician's understanding level and the gradual popularization of detection methods like metabolic screening and genetic gene examination.

It can be seen that in Fig. 9, the ANoP with encephalopathy has a downward trend from 2012 to 2020. Encephalopathy refers to the symptoms of brain dysfunction of the central nervous system. It is generally regarded as a symptomatic diagnosis when the cause has not been found. From the retrospective analysis, it can be inferred that the decline is inseparable from the improvement of the diagnosis level, and is also related to the gradually accurate diagnosis.

Compared the values from 2019 to 2020, we can see that the ANoP with intracranial infection decreases significantly in 2020, from 513 to 169. Moreover, ANoP with central nervous system autoimmune disease and genetic metabolic encephalopathy increase significantly in these two years. To be specific, the ANoP with bacterial intracranial infection is 216 in 2019 and decreases sharply to 72 in 2020. We consider several factors as follow: firstly, under strict epidemic prevention, the personnel mobility is greatly reduced, so that, as the main reason, it may cut off the transmission of bacteria and viruses; secondly, it is inconvenient to seek medical advice during covid-19, so they chose to deal with it in local hospitals; of course, the factor cannot be ignored is that the diagnostic level has improved. Many patients are diagnosed as atypical bacterial encephalitis before, but now diagnosed as other diseases, including immune encephalitis. Besides, there is an increasing ANoP about central nervous system disease. Whether it is related to COVID-19 attacking the central nervous system, we will do not discuss, but it can be used as a future research direction.

Based on mass data collected in the past decade, we systematically comb the neurology diseases of children, and divide them into 20 diseases (primary subjects) and 44 subtype diseases (secondary subjects) as shown in Fig. 1 in this paper.

Note that children with neurological diseases mainly suffer from encephalopathy and encephalitis, such as intracranial infection, central nervous system autoimmune disease, encephalopathy, cerebrovascular disease, and hereditary metabolic encephalopathy [7, 8]. In fact, these diseases are rather complex, and the medical level in this direction is still in continuous development. In the process of analyzing neurology diseases and tracing its development, we obtained some meaningful conclusions with theoretical value and gave corresponding suggestions as follow:

(1) In terms of basic characteristics: i) Among the neurological diseases in children, paroxysmal disease is the most common, such as epilepsy and febrile convulsion, and then intracranial infection followed. Apparently, many diseases also have higher incidences, with almost more than 100 cases per year, such as migraine/vascular neuromodulation disorder, facial paralysis, developmental disorder (neuropsychiatric), and neuromuscular junction disease in children. (ii) The average onset age of benign intracranial hypertension is the lowest, which is consistent with its characteristics, with the imperfect blood brain barrier in infants. Noted that the average age of convulsion (others) is close to that of febrile convulsion, suggesting that the two may have similar pathogenesis and epidemiological characteristics. Furthermore, in terms of mean onset age, the situation of intracranial infection of unknown cause is closer to viral intracranial infection rather than bacterial intracranial infection, indicating that a higher proportion of these cases is due to viral intracranial infection. Similarly, the factor, easily misdiagnosed mitochondrial encephalopathy as viral intracranial infection in the past, is also caused by their similar onset ages. Meanwhile, the onset age of central nervous system autoimmune disease is slightly higher than viral intracranial infection’s, suggesting that children beyond school age are more likely to develop secondary autoimmune disease. iii) The ratio of males in most neurology diseases is higher, which influenced by the fact that males outnumber females in the population. Nevertheless, the gender ratio is reversed in most central nervous system autoimmune disease, though this condition is a common phenomenon. iv) LoS for epilepsy (variance of 24.88, much higher than for other paroxysmal disease) varies greatly, which is mainly due to the great heterogeneity. For example, benign epilepsy in children with spike in the central temporal region, as a relatively simple condition, does not require difficult treatment, resulting in relatively low cost. Various epileptic encephalopathies, however, with complex etiology and frequent recurrence, their treatments are hard, which also lead to long hospitalization and high cost. v) Similarly, viral intracranial infection also exists such condition. Mild viral intracranial infection can be self-limited and can be cured without special treatment, but severe viral intracranial infection is disparate, since it may be seriously life-threatening and even secondary to autoimmune encephalitis. The acute critical period involves special means such as gamma-globulin, high dose hormone, plasma exchange and ventilator treatment. Therefore, the hospitalization time and cost of severe viral intracranial infection are also high. It is plausible that severe viral intracranial infection is mostly on the basis of virus infection, secondary immune storm, leading to the aggravation of the condition. However, limited to the current understanding and diagnosis and treatment level, these patients may not find the corresponding diagnostic markers. In this regard, the update of diagnostic methods will help to further confirm the accurate diagnosis of patients at an early stage, make more targeted treatment in time and improve the prognosis [9]. vi) Central nervous immune system disease tends to has longer hospitalization time and higher cost. It cannot be separated the severity of most immune diseases and the use of special treatments such as gamma globulin and plasma exchange in the acute stage; vii) There was no significant difference in LoS and ADHE between the two subtype diseases of hereditary metabolic encephalopathy, even the LoS differs only 0.1 days, which is related to the lack of effective treatment for most genetic diseases. viii) Based on the above results, the weak concentration of onset age of viral intracranial infection not only suggests that it can occur at all ages, but also that some other diseases may be misdiagnosed as it or intracranial infection of unknown etiology. In addition, the age of bacterial intracranial infection is relatively concentrated, reflecting that most clinical patients of this disease are small infants, and a few older children suffer from it mostly related to the factors such as immune deficiency and local structural abnormalities.

(2) It can be seen from the seasonal characteristics that there are great differences in high incidence seasons among different diseases. On the one hand, central nervous system infectious disease is mostly concentrated in the high temperature season, especially in summer, which are related to the growth and spread of viruses and bacteria in a high temperature environment. On the other hand, neuroimmune disease is more common in winter and spring, which is related to the reduction of autoimmune barrier function of the human body in cold conditions. Hence, the following measures effectively improve this situation. For instance, we should effectively strengthen the immune capacity of the body, pay attention to keeping children warm and release policies on central heating in a humid and cold environment in the southern part of China. Of course, in the future, more in-depth studies should be conducted on multi-center, multi-latitude patients for long-term monitoring.

(3) In terms of disease development, the number of hospitalized patients with intracranial infection and encephalopathy has decreased significantly in recent years, while that with most central nervous system autoimmune disease and hereditary metabolic encephalopathy maintains the trend of continuous growth year by year [10]. It is necessary to further explain this phenomenon. i) With the improvement of hygienic conditions, water quality and public health awareness, the number of patients with intracranial infection dwindles constantly. ii) The research on neurological diseases in China started late. In the past, we often misdiagnosed immune encephalitis and hereditary metabolic encephalopathy as viral intracranial infection and atypical bacterial intracranial infection. Nevertheless, medical diagnosis has improved and related diseases have become more detailed in recent years. To be specific, the detection of neuroimmune antibodies has been gradually carried out since 2015 [11] and we have been gradually popularized metabolic screening and genetic testing technology in recent 10 years [12], which lead to the increase number patients correctly diagnosed as central nervous system autoimmune disease and hereditary metabolic encephalopathy respectively. Meanwhile, these measures have effectively reduced the misclassification rate of the disease. They can be one of the reasons for the decrease in the number of patients with viral and bacterial intracranial infection. iii) At the same time, there is a significant decrease in the number of intracranial infection during the COVID-19 pandemic, especially the sudden decline of bacterial intracranial infection. The epidemic has brought a lot of inconvenience to patients' medical treatment, and the isolation at home is related to cutting off the transmission route of bacteria and virus. iv) The ANoP of central demyelinating disease dropped sharply in 2020. This also indicated that more and more patients, diagnosed as central demyelinating disease before, are now finely diagnosed as MOG antibody-associated disorder or anti-NMDAR encephalitis. Obviously, the improvement of diagnostic accuracy is conducive to the refinement of patient treatment plan and long-term management. Furthermore, in recent years, with the improvement of detection technology, medical workers have been discovered and reported new antibodies related to immune encephalitis on and on [13].

(4) Last but not least, for some diseases with only one central distribution trend, they basically have precise treatment plan, and the specific treatment after diagnosis can increase the prognostic value. However, the distributions of some disease characteristics are multicentric, and there is no corresponding precise treatment scheme. So these diseases are usually serious, with long time of admission and high cost. For example, many diseases in viral encephalitis with kinds of etiologies are unclear, and a certain proportion of them are unknown. Therefore, it is expected to improve this dilemma by further technological development.

And some central nervous system autoimmune disease characteristics show non-unimodal phenomenon, indicating that our understanding of these diseases is still limited. Though there have been dozens of immune encephalitis antibodies and the adjustment of clinical diagnosis and treatment plan has been updated accordingly over the past 20 years, many other antibody-specific encephalitis are highly likely to be discovered and diagnosed in the future. It is noted that the disease density distribution chart can give doctors a certain reminder in the refinement of the disease. Especially when the distribution of disease features is polycentric, we need to pay attention to whether the disease can be further classified. It is of great significance for the disease understanding, the adjustment of treatment plan and the improvement of prognosis.

Over the years, the disease pedigree of neurological diseases took place great changes. Diagnostic technologies such as immune antibody detection, metabolic examination and genetic gene detection have been improved, and doctors' diagnosis and treatment concepts are constantly updated. These advances make more and more difficult and rare diseases to be recognized, diagnosed and further treated accurately. Based on the research on the spectrum of neurological diseases in recent 8 years, we provided epidemiological data for common neurological diseases and confirmed the historical process of the change in nervous system disease spectrum under precision medicine. The number of patients with infection related diseases and uncertain diagnosis is decreasing, and the proportion of neuroimmune diseases and genetic metabolic diseases is increasing. In addition, according to the analysis, we put forward a conjecture: when the disease distribution is multicentric, the possibility and necessity of further segmentation of diseases need to be considered. Although the conjecture proposed in this paper lacks systematic operational tests at this stage, it puts forward the direction of refining the disease and looking for new diseases. It contributes to accurate diagnosis and formulate individualized treatment plans and reasonably allocate medical resources. Meanwhile, targeted treatment for some diseases, like genetic diseases, needs to be further strengthened. In addition, we should pay attention to the improvement of sanitation, living conditions and the awareness of daily health care.

Abbreviation	Full Names
LoS	Length of Stay
MNoP	Monthly Number of Patients
ANoP	Annual Number of Patients
ADHE	Average Daily Hospitalization Expense
MOG antibody-associated disorder	myelin oligodendrocyte glycoprotein-antibody-associated disorder
GFAP astrocytopathy	Autoimmune Glial Fibrillary Acidic Protein Astrocytopathy

Ethical Approval and Consent to participate

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Consent for publication

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Availability of data and materials

The datasets used during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests

Funding

This work was supported by grants from the National Natural Science Foundation of China (No. 82171453, 81671297), Natural Science Foundation of Hunan Province (2021JJ30393) and Huxiang Youth Talent Support Program (2021RC3117).

Authors' contributions

XHL and LWW prepared the study protocol, analyzed, and interpreted the data; and contributed to the first revision. HXC and HJY were responsible for the first draft and contributed statistical analyses. XSM, HYY and LA performed the data collection and processing. QL prepared the study protocol. All authors read and approved the final manuscript.

Acknowledgements

Not applicable

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The retrospective data analysis on the spectrum of nervous system diseases in children

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Abstract

Background

Methods

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1. Introduction

2. Materials And Methods

2.1 Study design

2.2 Variables description

2.3 A summary on the related statistical analysis

3. Results

3.1 Results of descriptive analysis

3.2 Results of heterogeneity analysis

3.3 Results of seasonal and trend Analysis

4. Discussion

5. Conclusion

List Of Abbreviations

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References

Additional Declarations

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